1. Field of the Invention
The present invention generally relates to implantable cardiac devices, including pacemakers, defibrillators and cardioverters, which stimulate cardiac tissue electrically to control the patient's heart rhythm. More particularly, the present invention relates to a method and apparatus for enhancing the sensing performance by means of morphological analysis of the intracardiac electrogram (IEGM) recorded by the implantable cardiac devices. The method disclosed in this invention is also applicable to devices and systems involving cardiac beat classification based on surface ECG analysis.
2. Description of the Related Art
Implantable cardiac devices, such as pacemakers, defibrillators and cardioverters, have been preferred therapy for treating various cardiac diseases, including bradyarrhythmia, tachyarrhythmia, heart failure, etc. Normal operation of these implantable cardiac devices requires reliable sensing of the cardiac electrical activity.
Currently, the sense detection is based on threshold crossing in all implantable pacemakers and ICDs. That is, a sense event is detected when the measured (and usually filtered) IEGM signal amplitude crosses above a predetermined sensing threshold. To minimize the problem of undersensing and oversensing, modern implantable cardiac devices can automatically adjust the sensing threshold (also known as sensitivity) so that it is adaptive to the IEGM signal amplitude.
However, the problems of oversensing and undersensing still exist, because the threshold crossing method alone could not resolve the sensing problems. Moreover, for sensed events, reliable event classification poses another challenge for the implant device.
Generally, the device sensed events could be classified into four classes: (1) normal intrinsic events, including normal atrial depolarization (P wave), and normal ventricular depolarization (QRS complex); (2) abnormal intrinsic depolarization, including ectopic atrial depolarization, ectopic ventricular depolarization, and retrograde P waves; (3) endogenous noise, including T waves, far-field R waves, far-field T waves, depolarization waveform double counting, and any other non-depolarization signal originating from within the heart; and (4) exogenous noise, including myopotentials, electromagnetic interference, lead failure artifact, in-channel and cross-channel pacing artifact, and any other signal originating from outside the heart.
For implantable cardiac devices, appropriate pacemaker timing and device diagnosis all depend on accurate event classifications. Particularly, the main task of event classification is to differentiate intrinsic events (classes 1 and 2) from non-intrinsic events (classes 3 and 4). Further classification of normal intrinsic events (class 1) from abnormal intrinsic events (class 2) is also desired.
Conventionally, the event classification in implantable cardiac devices is solely based on event timing information. As well known in the art, a plural of time intervals are defined (and mostly programmable) in the implantable cardiac device for event classification, such as atrial refractory period, ventricular refractory period, far-field blanking window, post-pace blanking window, and so on. A sense event outside the refractory period would be classified as an intrinsic event, whereas that inside the refractory period or a blanking window would be classified as a non-intrinsic event.
More complex algorithms were developed to improve the classification accuracy. For example, an atrial sense inside the atrial refractory period would be classified as an intrinsic P wave if it were followed by a normal ventricular sense event within a predefined time interval. On the other hand, atrial senses in the atrial refractory period following ventricular paces would be classified as retrograde P waves if the intervals from ventricular paces to the atrial senses are stable.
In another example, a ventricular sense outside the ventricular refractory period would be classified as a normal ventricular depolarization if it were preceded by an atrial event within a predefined time interval, or a ventricular extra-systole otherwise.
Yet in another example, when a device has no atrial sensing (e.g., in VVI mode), a ventricular sense outside the ventricular refractory period would be classified as a normal ventricular depolarization if the current ventricular coupling interval is not shorter than the average of preceding ventricular intervals by a predefined percentage (also called the prematurity index), otherwise a ventricular extra-systole is declared instead. Similar classification of normal atrial depolarization and atrial extra-systole can also be made.
All above event classification methods have the intrinsic limitation that only event timing information is utilized. As a result, the sensitivity and specificity of event classification is limited, and event misclassification is common in implantable cardiac devices. These limitations, on one hand, can potentially cause delay or withhold of appropriate therapies, and on the other hand, can potentially cause delivery of inappropriate therapies.
It is known that the morphology of the cardiac electric signal contains useful information for cardiac event classification. For example, the normal intrinsic cardiac depolarization usually has different morphology than that of the ectopic beat. Various pattern recognition techniques (e.g., neural network, fuzzy logic, etc.) have been developed for cardiac beat classification based on morphological analysis of the surface ECG signals. However, morphological analysis of IEGM is rarely used in the implantable cardiac devices for event classification, mainly due to the high complexity of these algorithms.
Generally, there are two different approaches for morphological analysis. In one approach, the morphology of the signal is characterized by a plural of metrics either directly measured from the signal (e.g., signal amplitude, width, area, slope, threshold crossing, peak polarity, etc.), or indirectly obtained from the transformed signal (e.g., Fourier transform, wavelet transform, symbolic and other nonlinear transforms, etc.). However, the caveat is that, in principle, the waveform morphology is unlikely to be fully characterized by a single or multiple metrics. In other words, metric-based approach usually results in loss of morphological information.
In another approach, the morphology of two signals can be compared directly by means of correlation analysis. High correlation between two signals indicates they have similar morphology, whereas low correlation indicates they have different morphology. The most commonly used correlation measure is the Pearson's Correlation Coefficient (PCC). However, it has two major limitations. First, the calculation of PCC requires floating point operation, which renders it not feasible for implementation in the low-power embedded systems, particularly the battery-powered implantable cardiac devices. Second, PCC does not account for the amplitude difference between signals and is sensitive to the impulsive noise.
In view of above, there is a need to provide the implantable cardiac devices a novel method to accurately, efficiently, and robustly perform IEGM morphology analysis, to facilitate sense event classification.